repo_name stringlengths 7 90 | path stringlengths 5 191 | copies stringlengths 1 3 | size stringlengths 4 6 | content stringlengths 976 581k | license stringclasses 15
values |
|---|---|---|---|---|---|
jamesmcm/luigi | examples/pyspark_wc.py | 17 | 3388 | # -*- coding: utf-8 -*-
#
# Copyright 2012-2015 Spotify AB
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law... | apache-2.0 |
andyraib/data-storage | python_scripts/env/lib/python3.6/site-packages/pandas/core/common.py | 7 | 15175 | """
Misc tools for implementing data structures
"""
import sys
import warnings
from datetime import datetime, timedelta
from functools import partial
import numpy as np
import pandas.lib as lib
import pandas.tslib as tslib
from pandas import compat
from pandas.compat import long, zip, iteritems
from pandas.core.confi... | apache-2.0 |
nomadcube/scikit-learn | examples/hetero_feature_union.py | 288 | 6236 | """
=============================================
Feature Union with Heterogeneous Data Sources
=============================================
Datasets can often contain components of that require different feature
extraction and processing pipelines. This scenario might occur when:
1. Your dataset consists of hetero... | bsd-3-clause |
alphaBenj/zipline | tests/data/test_resample.py | 1 | 34189 | # Copyright 2016 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writ... | apache-2.0 |
davidthaler/arboretum | arboretum/datasets/load_data.py | 1 | 2875 | '''
Load functions for datasets that load and split data, ensuring correct
dtype for arboretum (ndarray of float), and splitting larger datasets
into train/test folds.
author: David Thaler
date: September 2017
'''
import numpy as np
import pandas as pd
import os
DATA_DIR = os.path.join(os.path.split(__file__)[0], 'da... | mit |
PythonProgramming/Support-Vector-Machines---Basics-and-Fundamental-Investing-Project | p12.py | 1 | 8235 | import pandas as pd
import os
import time
from datetime import datetime
import re
from time import mktime
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import style
style.use("dark_background")
# path = "X:/Backups/intraQuarter" # for Windows with X files :)
# if git clone'ed then use relative path... | mit |
0asa/sparklingpandas | sparklingpandas/test/sparklingpandastestcase.py | 1 | 3828 | #
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not us... | apache-2.0 |
hsuantien/scikit-learn | examples/decomposition/plot_pca_3d.py | 354 | 2432 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
Principal components analysis (PCA)
=========================================================
These figures aid in illustrating how a point cloud
can be very flat in one direction--which is where PCA
comes in to ch... | bsd-3-clause |
Gamecredits-Universe/Gamecredits-electrum-client | plugins/__init__.py | 1 | 4157 | #!/usr/bin/env python
#
# Electrum - lightweight Bitcoin client
# Copyright (C) 2015 Thomas Voegtlin
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at y... | gpl-3.0 |
subutai/nupic.research | projects/continuous_learning/correlation_experiment.py | 3 | 12568 | # ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2020, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This p... | agpl-3.0 |
tosolveit/scikit-learn | sklearn/metrics/ranking.py | 44 | 25479 | """Metrics to assess performance on classification task given scores
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Function named as ``*_error`` or ``*_loss`` return a scalar value to minimize:
the lower the better
"""
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.... | bsd-3-clause |
robclewley/compneuro | phaseplane.py | 1 | 189488 | """
Phase plane utilities.
Some 2011 functionality has not yet been updated to use the plotter
phase plane plotting manager.
IMPORTANT NOTE DURING DEVELOPMENT:
For now, many operations with nullclines assume that they are NOT multi-valued
as a function of their variables, and that they are monotonic only... | bsd-3-clause |
deo1/deo1 | KaggleKkboxChurn/xgbhelpers.py | 2 | 2936 | import pandas as pd
def get_params(algorithm, ptype, ver):
import params as p
if algorithm == 'xgb':
if ptype == 'start':
if ver == 1: param = p.Xgbparams1()
if ver == 2: param = p.Xgbparams2()
if ver == 3: param = p.Xgbparams3()
if ver == 4: param = p.X... | mit |
rvraghav93/scikit-learn | examples/cluster/plot_kmeans_stability_low_dim_dense.py | 338 | 4324 | """
============================================================
Empirical evaluation of the impact of k-means initialization
============================================================
Evaluate the ability of k-means initializations strategies to make
the algorithm convergence robust as measured by the relative stan... | bsd-3-clause |
bhargavasana/activitysim | activitysim/defaults/tables/size_terms.py | 1 | 1805 | import os
import orca
import pandas as pd
def size_term(land_use, destination_choice_coeffs):
"""
This method takes the land use data and multiplies various columns of the
land use data by coefficients from the spec table in order
to yield a size term (a linear combination of land use variables).
... | agpl-3.0 |
altairpearl/scikit-learn | examples/applications/plot_stock_market.py | 76 | 8522 | """
=======================================
Visualizing the stock market structure
=======================================
This example employs several unsupervised learning techniques to extract
the stock market structure from variations in historical quotes.
The quantity that we use is the daily variation in quote ... | bsd-3-clause |
varunkothamachu/seldon-server | external/predictor/python/seldon/pipeline/pipelines.py | 5 | 11288 | import seldon.fileutil as fu
import json
from sklearn.externals import joblib
import os.path
import logging
import shutil
import unicodecsv
class Feature_transform(object):
"""Base feature transformation class with method defaults
"""
def __init__(self):
self.pos = 0
self.input_feature = "... | apache-2.0 |
iamApalive/private | bin/sensor.py | 1 | 1640 | !/usr/bin/python
import pandas as pd
import pandas_profiling
import numpy as np
#Initializing PySpark
from pyspark import SparkContext, SparkConf
from pyspark.sql import SQLContext
from sklearn.linear_model import LinearRegression,LogisticRegression
class sensor:
def __init__(self,file=False,json=False):
... | apache-2.0 |
aakhundov/tf-attend-infer-repeat | embeddings.py | 1 | 7241 | import os
import math
import shutil
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector
from air.air_model import AIRModel
from demo.model_wrapper import ModelWrapper
from mu... | mit |
shyamalschandra/scikit-learn | sklearn/preprocessing/data.py | 9 | 67092 | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Mathieu Blondel <mathieu@mblondel.org>
# Olivier Grisel <olivier.grisel@ensta.org>
# Andreas Mueller <amueller@ais.uni-bonn.de>
# Eric Martin <eric@ericmart.in>
# Giorgio Patrini <giorgio.patrini@anu.edu.au>
# Lic... | bsd-3-clause |
wasade/networkx | examples/drawing/chess_masters.py | 54 | 5146 | #!/usr/bin/env python
"""
An example of the MultiDiGraph clas
The function chess_pgn_graph reads a collection of chess
matches stored in the specified PGN file
(PGN ="Portable Game Notation")
Here the (compressed) default file ---
chess_masters_WCC.pgn.bz2 ---
contains all 685 World Chess Championship matches
from... | bsd-3-clause |
andrew-lundgren/detchar | cbc_dq/snr_variance.py | 1 | 1252 | #! /usr/bin/env python
from gwpy.timeseries import TimeSeries
from numpy import *
import sys
ifo = sys.argv[1]
chan = ifo + ":GDS-CALIB_STRAIN"
st = int(sys.argv[2])
dur = 2048
fmin, fmax = 30., 1024.
chunk, step = 64, 16
fftlen, overlap = 16, 8
data = TimeSeries.fetch(chan, st, st+dur, verbose=True)
srate = data.s... | gpl-3.0 |
shyamalschandra/scikit-learn | examples/plot_kernel_ridge_regression.py | 39 | 6259 | """
=============================================
Comparison of kernel ridge regression and SVR
=============================================
Both kernel ridge regression (KRR) and SVR learn a non-linear function by
employing the kernel trick, i.e., they learn a linear function in the space
induced by the respective k... | bsd-3-clause |
JSLBen/KnowledgeTracing | reference_py/tf_RNN.py | 1 | 15676 | # tensorflow version: 1.0.0
import numpy as np
import tensorflow as tf
from numpy.random import permutation as perm
import pandas as pd
class tf_RNN(object):
"""
RNN classifier with LSTM cells with tensorflow
"""
def __init__(self,
num_features,
... | mit |
glwagner/py2Periodic | py2Periodic/physics/twoDimTurbulence_fftbuildEx.py | 1 | 8847 | import numpy as np
import numexpr as ne
import time as timeTools
import matplotlib.pyplot as plt
from ..doublyPeriodic import doublyPeriodicModel
from numpy import pi
class model(doublyPeriodicModel):
def __init__(self, name = None,
# Grid parameters
nx = 128, ny = None, Lx = 2.0*pi, Ly ... | mit |
marcdata/pynba-tfo | tfo_story_figures_v2.py | 1 | 15216 |
# TFO Story Figures
#
# Go ahead and collect subset of figures, for displaying main points of results, analysis.
# This has some more annotation and formatting to it. So it's meant to be run
# after all the data has been cycled thru, calculations made, etc. Offsets, etc
# will be dependent on the values.
# Imp... | gpl-2.0 |
newemailjdm/scipy | doc/source/tutorial/examples/newton_krylov_preconditioning.py | 99 | 2489 | import numpy as np
from scipy.optimize import root
from scipy.sparse import spdiags, kron
from scipy.sparse.linalg import spilu, LinearOperator
from numpy import cosh, zeros_like, mgrid, zeros, eye
# parameters
nx, ny = 75, 75
hx, hy = 1./(nx-1), 1./(ny-1)
P_left, P_right = 0, 0
P_top, P_bottom = 1, 0
def get_precon... | bsd-3-clause |
sssundar/Drone | simulator/plant.py | 1 | 20549 | import sys
import numpy as np
from scipy.integrate import odeint
from quaternions import *
from matplotlib import pyplot as plt
from animate import *
# See Quad notes from 7/31/2018-8/13/2018 for derivations.
class Plant(object):
#
# @brief Initializes a horizontal quad oriented with the Earth's magnetic
#... | gpl-3.0 |
mayblue9/scikit-learn | sklearn/cluster/tests/test_birch.py | 342 | 5603 | """
Tests for the birch clustering algorithm.
"""
from scipy import sparse
import numpy as np
from sklearn.cluster.tests.common import generate_clustered_data
from sklearn.cluster.birch import Birch
from sklearn.cluster.hierarchical import AgglomerativeClustering
from sklearn.datasets import make_blobs
from sklearn.l... | bsd-3-clause |
alsrgv/tensorflow | tensorflow/contrib/learn/python/learn/estimators/estimator_input_test.py | 46 | 13101 | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | apache-2.0 |
poryfly/scikit-learn | examples/model_selection/plot_confusion_matrix.py | 244 | 2496 | """
================
Confusion matrix
================
Example of confusion matrix usage to evaluate the quality
of the output of a classifier on the iris data set. The
diagonal elements represent the number of points for which
the predicted label is equal to the true label, while
off-diagonal elements are those that ... | bsd-3-clause |
IshankGulati/scikit-learn | examples/tree/plot_tree_regression.py | 95 | 1516 | """
===================================================================
Decision Tree Regression
===================================================================
A 1D regression with decision tree.
The :ref:`decision trees <tree>` is
used to fit a sine curve with addition noisy observation. As a result, it
learns ... | bsd-3-clause |
evan-magnusson/dynamic | Data/Calibration/Firm_Calibration_Python/data/soi/processing/pull_soi_proprietorship.py | 4 | 8182 | '''
SOI Proprietorship Tax Data (pull_soi_proprietorship.py):
-------------------------------------------------------------------------------
Last updated: 6/29/2015.
This module creates functions for pulling the proprietorship soi tax data into
NAICS trees.
'''
# Packages:
import os.path
import numpy as np
import pan... | mit |
yyjiang/scikit-learn | sklearn/linear_model/tests/test_sgd.py | 129 | 43401 | import pickle
import unittest
import numpy as np
import scipy.sparse as sp
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing ... | bsd-3-clause |
MohammedWasim/scikit-learn | benchmarks/bench_tree.py | 297 | 3617 | """
To run this, you'll need to have installed.
* scikit-learn
Does two benchmarks
First, we fix a training set, increase the number of
samples to classify and plot number of classified samples as a
function of time.
In the second benchmark, we increase the number of dimensions of the
training set, classify a sam... | bsd-3-clause |
jaidevd/scikit-learn | examples/calibration/plot_calibration.py | 66 | 4795 | """
======================================
Probability calibration of classifiers
======================================
When performing classification you often want to predict not only
the class label, but also the associated probability. This probability
gives you some kind of confidence on the prediction. However,... | bsd-3-clause |
jriehl/numba | numba/tests/pdlike_usecase.py | 3 | 8766 | """
Implementation of a minimal Pandas-like API.
"""
import numpy as np
from numba import types, cgutils
from numba.datamodel import models
from numba.extending import (
typeof_impl, type_callable, register_model,
lower_builtin, box, unbox, NativeValue,
overload, overload_attribute, overload_method, make_... | bsd-2-clause |
trangnm58/idrec | IdRecDemo/idocr/recognition/model_handler.py | 1 | 4401 | from __future__ import division, print_function, unicode_literals
import sys
from os import listdir
import numpy as np
from keras.models import model_from_json
from sklearn.metrics import f1_score, classification_report, confusion_matrix
from sklearn.model_selection import KFold, train_test_split
sys.path.insert(0, ".... | mit |
r-kamath/zeppelin | python/src/main/resources/python/mpl_config.py | 41 | 3653 | # Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use ... | apache-2.0 |
petosegan/scikit-learn | sklearn/feature_extraction/image.py | 263 | 17600 | """
The :mod:`sklearn.feature_extraction.image` submodule gathers utilities to
extract features from images.
"""
# Authors: Emmanuelle Gouillart <emmanuelle.gouillart@normalesup.org>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Olivier Grisel
# Vlad Niculae
# License: BSD 3 clause
fro... | bsd-3-clause |
wazaahhh/pgames | python_prototype/abm.py | 1 | 25488 | #from matplotlib import use, get_backend
#if 'Agg' != get_backend().title():
# use('Agg')
import sys
import numpy as np
#import pylab as pl
from random import choice,randrange, shuffle
import time
from datetime import datetime
import json
import boto
global bucketName
bucketName = "property_game"
def S3connectBuc... | mit |
xiaoxiamii/scikit-learn | sklearn/datasets/species_distributions.py | 198 | 7923 | """
=============================
Species distribution dataset
=============================
This dataset represents the geographic distribution of species.
The dataset is provided by Phillips et. al. (2006).
The two species are:
- `"Bradypus variegatus"
<http://www.iucnredlist.org/apps/redlist/details/3038/0>`_... | bsd-3-clause |
jwplayer/jwalk | setup.py | 1 | 2598 | # -*- coding: utf-8 -*-
"""Distutils setup file, used to install or test 'jwalk'."""
from __future__ import print_function
import sys
import textwrap
import pkg_resources
from setuptools import setup, find_packages, Extension
with open('README.rst') as f:
readme = f.read()
def is_installed(requirement):
try... | apache-2.0 |
luo66/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
Ziqi-Li/bknqgis | pandas/pandas/tests/indexing/test_partial.py | 5 | 20611 | """
test setting *parts* of objects both positionally and label based
TOD: these should be split among the indexer tests
"""
import pytest
from warnings import catch_warnings
import numpy as np
import pandas as pd
from pandas import Series, DataFrame, Panel, Index, date_range
from pandas.util import testing as tm
... | gpl-2.0 |
cweinschenk/cweinschenk | Map_Plotting/NIST_Studies/lodd_map.py | 1 | 2521 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
import numpy as np
import pandas as pd
from bokeh.util.browser import view
from bokeh.document import Document
from bokeh.embed import file_html
from bokeh.models.glyphs import Circle
from bokeh.plotting import figure, show, output_file
from bokeh.models i... | mit |
Windy-Ground/scikit-learn | sklearn/ensemble/tests/test_weight_boosting.py | 83 | 17276 | """Testing for the boost module (sklearn.ensemble.boost)."""
import numpy as np
from sklearn.utils.testing import assert_array_equal, assert_array_less
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal, assert_true
from sklearn.utils.testing import assert_raises... | bsd-3-clause |
btabibian/scikit-learn | sklearn/model_selection/tests/test_validation.py | 7 | 42247 | """Test the validation module"""
from __future__ import division
import sys
import warnings
import tempfile
import os
from time import sleep
import numpy as np
from scipy.sparse import coo_matrix, csr_matrix
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_false
from sklearn.uti... | bsd-3-clause |
iproduct/course-social-robotics | 11-dnn-keras/venv/Lib/site-packages/pandas/tests/indexes/multi/test_duplicates.py | 3 | 10245 | from itertools import product
import numpy as np
import pytest
from pandas._libs import hashtable
from pandas import DatetimeIndex, MultiIndex
import pandas._testing as tm
@pytest.mark.parametrize("names", [None, ["first", "second"]])
def test_unique(names):
mi = MultiIndex.from_arrays([[1, 2, 1, 2], [1, 1, 1,... | gpl-2.0 |
cogeorg/BlackRhino | networkx/convert.py | 3 | 13008 | """Functions to convert NetworkX graphs to and from other formats.
The preferred way of converting data to a NetworkX graph is through the
graph constuctor. The constructor calls the to_networkx_graph() function
which attempts to guess the input type and convert it automatically.
Examples
--------
Create a graph wit... | gpl-3.0 |
shahankhatch/scikit-learn | examples/text/hashing_vs_dict_vectorizer.py | 284 | 3265 | """
===========================================
FeatureHasher and DictVectorizer Comparison
===========================================
Compares FeatureHasher and DictVectorizer by using both to vectorize
text documents.
The example demonstrates syntax and speed only; it doesn't actually do
anything useful with the e... | bsd-3-clause |
atantet/transferPlasim | statistics/plotCCF.py | 1 | 3878 | import os
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import atmath
# Define the observable
srcDir = '../runPlasim/postprocessor/indices/'
# SRng = np.array([1260, 1360, 1380, 1400, 1415, 1425, 1430, 1433,
# 1263, 1265, 1270, 1280, 1300, 1330, 1360, 1435])
# restartStat... | gpl-2.0 |
glennq/scikit-learn | sklearn/mixture/tests/test_gaussian_mixture.py | 26 | 40216 | # Author: Wei Xue <xuewei4d@gmail.com>
# Thierry Guillemot <thierry.guillemot.work@gmail.com>
# License: BSD 3 clauseimport warnings
import sys
import warnings
import numpy as np
from scipy import stats, linalg
from sklearn.covariance import EmpiricalCovariance
from sklearn.datasets.samples_generator import... | bsd-3-clause |
RPGOne/Skynet | scikit-learn-c604ac39ad0e5b066d964df3e8f31ba7ebda1e0e/doc/sphinxext/gen_rst.py | 2 | 38923 | """
Example generation for the scikit learn
Generate the rst files for the examples by iterating over the python
example files.
Files that generate images should start with 'plot'
"""
from __future__ import division, print_function
from time import time
import ast
import os
import re
import shutil
import traceback
i... | bsd-3-clause |
hanfang/glmnet_python | test/example_gaussian.py | 1 | 3451 | # Import relevant modules and setup for calling glmnet
import sys
sys.path.append('../test')
sys.path.append('../lib')
import scipy
import importlib
import matplotlib.pyplot as plt
import glmnet
import glmnetPlot
import glmnetPrint
import glmnetCoef
import glmnetPredict
import cvglmnet
import cvglmnetCoef
import c... | gpl-2.0 |
aclapes/simpledarwintree | simpledarwintree.py | 1 | 51564 | from scipy.io import loadmat
from os import listdir,makedirs,remove
from os.path import isfile,join,splitext,exists,basename
import numpy as np
from sklearn import preprocessing, svm, multiclass, metrics, cross_validation
import itertools
from joblib import delayed, Parallel
import cPickle
import sys
import random
impo... | gpl-2.0 |
awlange/brainsparks | tests/particle333_test.py | 1 | 15864 | """
Script entry point
"""
from src.calrissian.particle333_network import Particle333Network
from src.calrissian.layers.particle333 import Particle333
from src.calrissian.optimizers.particle333_sgd import Particle333SGD
from multiprocessing import Pool
import numpy as np
import time
import pandas as pd
import pickle
... | mit |
xyguo/scikit-learn | sklearn/utils/graph.py | 289 | 6239 | """
Graph utilities and algorithms
Graphs are represented with their adjacency matrices, preferably using
sparse matrices.
"""
# Authors: Aric Hagberg <hagberg@lanl.gov>
# Gael Varoquaux <gael.varoquaux@normalesup.org>
# Jake Vanderplas <vanderplas@astro.washington.edu>
# License: BSD 3 clause
impo... | bsd-3-clause |
johnmgregoire/JCAPdatavis | echem_stacked_tern4.py | 1 | 51062 | import matplotlib.cm as cm
import numpy
import pylab
import h5py, operator, copy, os, csv, sys
from echem_plate_fcns import *
from echem_plate_math import *
PyCodePath=os.path.split(os.path.split(os.path.realpath(__file__))[0])[0]
sys.path.append(os.path.join(PyCodePath,'ternaryplot'))
from myternaryutility import Ter... | bsd-3-clause |
procoder317/scikit-learn | sklearn/datasets/tests/test_svmlight_format.py | 228 | 11221 | from bz2 import BZ2File
import gzip
from io import BytesIO
import numpy as np
import os
import shutil
from tempfile import NamedTemporaryFile
from sklearn.externals.six import b
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert... | bsd-3-clause |
trankmichael/scikit-learn | sklearn/linear_model/tests/test_logistic.py | 105 | 26588 | import numpy as np
import scipy.sparse as sp
from scipy import linalg, optimize, sparse
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.util... | bsd-3-clause |
jakevdp/scipy | scipy/signal/fir_filter_design.py | 17 | 36232 | # -*- coding: utf-8 -*-
"""Functions for FIR filter design."""
from __future__ import division, print_function, absolute_import
from math import ceil, log
import warnings
import numpy as np
from numpy.fft import irfft, fft, ifft
from scipy.special import sinc
from scipy.linalg import toeplitz, hankel, pinv
from scipy... | bsd-3-clause |
yipenggao/moose | modules/tensor_mechanics/test/tests/capped_mohr_coulomb/small_deform_23_24.py | 4 | 1686 | #!/usr/bin/env python
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
def expected(fn):
f = open(fn, "r")
data = sorted([map(float, line.strip().split(",")[3:6]) for line in f.readlines()[1:]])
data = [[d[0], d[1], d[2]] for d in data if d[0] >= d[1] and d[1] >= d[2]]
mean = [(... | lgpl-2.1 |
MatthieuBizien/scikit-learn | sklearn/base.py | 11 | 18381 | """Base classes for all estimators."""
# Author: Gael Varoquaux <gael.varoquaux@normalesup.org>
# License: BSD 3 clause
import copy
import warnings
import numpy as np
from scipy import sparse
from .externals import six
from .utils.fixes import signature
from .utils.deprecation import deprecated
from .exceptions impo... | bsd-3-clause |
mayblue9/scikit-learn | setup.py | 76 | 9370 | #! /usr/bin/env python
#
# Copyright (C) 2007-2009 Cournapeau David <cournape@gmail.com>
# 2010 Fabian Pedregosa <fabian.pedregosa@inria.fr>
# License: 3-clause BSD
descr = """A set of python modules for machine learning and data mining"""
import sys
import os
import shutil
from distutils.command.clean ... | bsd-3-clause |
charanpald/wallhack | wallhack/viroscopy/model/ProcessResults.py | 1 | 17156 | import numpy
import logging
import sys
import multiprocessing
import os
from apgl.graph.GraphStatistics import GraphStatistics
from sandbox.util.PathDefaults import PathDefaults
from sandbox.util.Util import Util
from sandbox.util.Latex import Latex
from sandbox.util.FileLock import FileLock
from sandbox.predictor... | gpl-3.0 |
AlexRobson/scikit-learn | examples/applications/plot_out_of_core_classification.py | 255 | 13919 | """
======================================================
Out-of-core classification of text documents
======================================================
This is an example showing how scikit-learn can be used for classification
using an out-of-core approach: learning from data that doesn't fit into main
memory. ... | bsd-3-clause |
fernandezcuesta/t4Monitor | test/unit_tests/test_dftools.py | 2 | 11466 | #!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
*t4mon* - T4 monitoring **test functions** for df_tools.py
"""
from __future__ import absolute_import
import tempfile
import unittest
import numpy as np
import pandas as pd
import pytest
from t4mon import df_tools, collector
from pandas.util.testing import assert_fra... | mit |
crichardson17/starburst_atlas | Low_resolution_sims/DustFree_LowRes/Padova_cont_subsolar_.2/UV1.py | 33 | 7340 | import csv
import matplotlib.pyplot as plt
from numpy import *
import scipy.interpolate
import math
from pylab import *
from matplotlib.ticker import MultipleLocator, FormatStrFormatter
import matplotlib.patches as patches
from matplotlib.path import Path
import os
# --------------------------------------------------... | gpl-2.0 |
djgagne/scikit-learn | examples/ensemble/plot_feature_transformation.py | 67 | 4285 | """
===============================================
Feature transformations with ensembles of trees
===============================================
Transform your features into a higher dimensional, sparse space. Then
train a linear model on these features.
First fit an ensemble of trees (totally random trees, a rand... | bsd-3-clause |
dominikwille/comp | sheet4/a2.py | 1 | 3143 | #!/usr/local/bin/python
# -*- coding: utf-8 -*-
#
# @author Dominik Wille
# @author Stefan Pojtinger
# @tutor Alexander Schlaich
# @sheet 4
#
# Bitte die plots zum testen einkommentieren.
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import matplotlib.pyplot as plt
#4.2
def phi(x, y):
return x**4 - x... | bsd-2-clause |
Jimmy-Morzaria/scikit-learn | examples/plot_kernel_approximation.py | 262 | 8004 | """
==================================================
Explicit feature map approximation for RBF kernels
==================================================
An example illustrating the approximation of the feature map
of an RBF kernel.
.. currentmodule:: sklearn.kernel_approximation
It shows how to use :class:`RBFSa... | bsd-3-clause |
zorojean/scikit-learn | examples/neighbors/plot_kde_1d.py | 347 | 5100 | """
===================================
Simple 1D Kernel Density Estimation
===================================
This example uses the :class:`sklearn.neighbors.KernelDensity` class to
demonstrate the principles of Kernel Density Estimation in one dimension.
The first plot shows one of the problems with using histogram... | bsd-3-clause |
hainm/scikit-learn | examples/ensemble/plot_partial_dependence.py | 249 | 4456 | """
========================
Partial Dependence Plots
========================
Partial dependence plots show the dependence between the target function [1]_
and a set of 'target' features, marginalizing over the
values of all other features (the complement features). Due to the limits
of human perception the size of t... | bsd-3-clause |
jaeilepp/mne-python | examples/decoding/plot_linear_model_patterns.py | 2 | 4328 | # -*- coding: utf-8 -*-
"""
===============================================================
Linear classifier on sensor data with plot patterns and filters
===============================================================
Decoding, a.k.a MVPA or supervised machine learning applied to MEG and EEG
data in sensor space. Fi... | bsd-3-clause |
griffinfoster/pulsar-polarization-sims | scripts/plotLineValVsTint.py | 1 | 5743 | #!/usr/bin/env python
"""
"""
import os,sys
import numpy as np
import matplotlib
#matplotlib.use('Agg')
import pylab as p
import cPickle as pkl
from scipy import interpolate
matplotlib.rc('xtick',labelsize=25)
matplotlib.rc('ytick',labelsize=25)
modeTitle=['Total Intensity','Invariant Interval','Matrix Template Ma... | mit |
vijaysbhat/incubator-airflow | airflow/hooks/presto_hook.py | 22 | 3617 | # -*- coding: utf-8 -*-
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
... | apache-2.0 |
mattsmart/biomodels | transcriptome_clustering/pitchfork_paramvary.py | 1 | 12624 | import matplotlib.pyplot as plt
import numpy as np
import os
import time
from inference import solve_true_covariance_from_true_J
from pitchfork_langevin import jacobian_pitchfork, steadystate_pitchfork, langevin_dynamics
from settings import DEFAULT_PARAMS, PARAMS_ID, FOLDER_OUTPUT, TIMESTEP, INIT_COND, NUM_TRAJ, NUM_... | mit |
azjps/bokeh | bokeh/charts/builder.py | 1 | 27190 | """This is the Bokeh charts interface. It gives you a high level API to build
complex plot is a simple way.
This is the Builder class, a minimal prototype class to build more chart
types on top of it.
"""
#-----------------------------------------------------------------------------
# Copyright (c) 2012 - 2014, Contin... | bsd-3-clause |
yugangzhang/scikit-beam | doc/sphinxext/plot_generator.py | 9 | 10081 | """
Sphinx plugin to run example scripts and create a gallery page.
Taken from seaborn project, which is turn was lightly
modified from the mpld3 project.
"""
from __future__ import division
import os
import os.path as op
import re
import glob
import token
import tokenize
import shutil
import json
import matplotlib
... | bsd-3-clause |
arrabito/DIRAC | Core/Utilities/Graphs/LineGraph.py | 5 | 4402 | ########################################################################
# $HeadURL$
########################################################################
""" LineGraph represents line graphs both simple and stacked. It includes
also cumulative graph functionality.
The DIRAC Graphs package is derived from ... | gpl-3.0 |
awanke/bokeh | bokeh/models/sources.py | 7 | 11155 | from __future__ import absolute_import
from ..plot_object import PlotObject
from ..properties import HasProps
from ..properties import Any, Int, String, Instance, List, Dict, Either, Bool, Enum
from ..validation.errors import COLUMN_LENGTHS
from .. import validation
from ..util.serialization import transform_column_so... | bsd-3-clause |
ChanChiChoi/scikit-learn | sklearn/utils/arpack.py | 265 | 64837 | """
This contains a copy of the future version of
scipy.sparse.linalg.eigen.arpack.eigsh
It's an upgraded wrapper of the ARPACK library which
allows the use of shift-invert mode for symmetric matrices.
Find a few eigenvectors and eigenvalues of a matrix.
Uses ARPACK: http://www.caam.rice.edu/software/ARPACK/
"""
#... | bsd-3-clause |
great-expectations/great_expectations | great_expectations/expectations/core/expect_column_values_to_be_dateutil_parseable.py | 1 | 5471 | from datetime import datetime
from typing import Dict, List, Optional, Union
import dateutil
import numpy as np
import pandas as pd
from dateutil.parser import parse
from great_expectations.core.expectation_configuration import ExpectationConfiguration
from great_expectations.execution_engine import (
ExecutionEn... | apache-2.0 |
shenzebang/scikit-learn | examples/calibration/plot_calibration_curve.py | 225 | 5903 | """
==============================
Probability Calibration curves
==============================
When performing classification one often wants to predict not only the class
label, but also the associated probability. This probability gives some
kind of confidence on the prediction. This example demonstrates how to di... | bsd-3-clause |
Mushirahmed/gnuradio | gr-utils/src/python/plot_data.py | 17 | 5768 | #
# Copyright 2007,2008,2011 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later ve... | gpl-3.0 |
ThyrixYang/LearningNotes | MOOC/stanford_cnn_cs231n/assignment1/cs231n/classifiers/neural_net.py | 1 | 10866 | from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
from past.builtins import xrange
class TwoLayerNet(object):
"""
A two-layer fully-connected neural network. The net has an input dimension of
N, a hidden layer dimension of H, and performs classification over C classes.
W... | gpl-3.0 |
bikong2/scikit-learn | examples/cluster/plot_cluster_iris.py | 350 | 2593 | #!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=========================================================
K-means Clustering
=========================================================
The plots display firstly what a K-means algorithm would yield
using three clusters. It is then shown what the effect of a bad
initializa... | bsd-3-clause |
Kate-Willett/HadISDH_Build | IndirectPHA_JAN2015.py | 1 | 67348 | #!/usr/local/sci/bin/python
# PYTHON3
#
# Author: Kate Willett
# Created: 11 October 2013
# Last update: 5 February 2018
# Location: /data/local/hadkw/HADCRUH2/UPDATE2015/PROGS/HADISDH_BUILD/
# GitHub: https://github.com/Kate-Willett/HadISDH_Build
# -----------------------
# CODE PURPOSE AND OUTPUT
# -----------... | cc0-1.0 |
mikofski/pvlib-python | pvlib/tests/test_modelchain.py | 1 | 83306 | import sys
import numpy as np
import pandas as pd
from pvlib import iam, modelchain, pvsystem, temperature, inverter
from pvlib.modelchain import ModelChain
from pvlib.pvsystem import PVSystem
from pvlib.tracking import SingleAxisTracker
from pvlib.location import Location
from pvlib._deprecation import pvlibDeprecat... | bsd-3-clause |
fenderglass/Nano-Align | plotting/mixture.py | 1 | 2698 | #!/usr/bin/env python2.7
#(c) 2015-2016 by Authors
#This file is a part of Nano-Align program.
#Released under the BSD license (see LICENSE file)
"""
Plots blockade frequency distributions of multiple datasets
"""
from __future__ import print_function
import sys
import os
import numpy as np
import matplotlib.pyplot... | bsd-2-clause |
siutanwong/scikit-learn | sklearn/metrics/cluster/supervised.py | 207 | 27395 | """Utilities to evaluate the clustering performance of models
Functions named as *_score return a scalar value to maximize: the higher the
better.
"""
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Wei LI <kuantkid@gmail.com>
# Diego Molla <dmolla-aliod@gmail.com>
# License: BSD 3 clause
fr... | bsd-3-clause |
vermouthmjl/scikit-learn | examples/applications/plot_prediction_latency.py | 85 | 11395 | """
==================
Prediction Latency
==================
This is an example showing the prediction latency of various scikit-learn
estimators.
The goal is to measure the latency one can expect when doing predictions
either in bulk or atomic (i.e. one by one) mode.
The plots represent the distribution of the pred... | bsd-3-clause |
JT5D/scikit-learn | doc/conf.py | 3 | 7503 | # -*- coding: utf-8 -*-
#
# scikit-learn documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 09:13:42 2010.
#
# This file is execfile()d with the current directory set to its containing
# dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
... | bsd-3-clause |
vivekmishra1991/scikit-learn | examples/datasets/plot_random_dataset.py | 348 | 2254 | """
==============================================
Plot randomly generated classification dataset
==============================================
Plot several randomly generated 2D classification datasets.
This example illustrates the :func:`datasets.make_classification`
:func:`datasets.make_blobs` and :func:`datasets.... | bsd-3-clause |
mattjj/pyhawkes | data/chalearn/preprocess.py | 2 | 8562 | """
Preprocess the fluorescence traces to get a spike train matrix.
Based on the ChaLearn Connectomics challenge starter kit
by Bisakha Ray, Javier Orlandi and Olav Stetter. I tried to clean it
up since it didn't make use of numpy functions and it was woefully
lacking in useful comments.
"""
import os
import sys
import... | mit |
pf4d/wood_pyrolysis | diblasi/helper.py | 1 | 9753 | from colored import fg, attr
from pylab import *
from fenics import *
from ufl import indexed
from matplotlib import colors, ticker
from matplotlib.ticker import LogFormatter, ScalarFormatter
from mpl_toolkits.axes_grid1 import ma... | gpl-3.0 |
magne-max/zipline-ja | tests/calendars/test_cfe_calendar.py | 1 | 1497 | from unittest import TestCase
import pandas as pd
from .test_trading_calendar import ExchangeCalendarTestBase
from zipline.utils.calendars.exchange_calendar_cfe import CFEExchangeCalendar
class CFECalendarTestCase(ExchangeCalendarTestBase, TestCase):
answer_key_filename = "cfe"
calendar_class = CFEExchangeCa... | apache-2.0 |
smartscheduling/scikit-learn-categorical-tree | sklearn/tests/test_naive_bayes.py | 142 | 17496 | import pickle
from io import BytesIO
import numpy as np
import scipy.sparse
from sklearn.datasets import load_digits, load_iris
from sklearn.cross_validation import cross_val_score, train_test_split
from sklearn.externals.six.moves import zip
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.te... | bsd-3-clause |
crisbarros/trading-with-python | lib/extra.py | 77 | 2540 | '''
Created on Apr 28, 2013
Copyright: Jev Kuznetsov
License: BSD
'''
from __future__ import print_function
import sys
import urllib
import os
import xlrd # module for excel file reading
import pandas as pd
class ProgressBar:
def __init__(self, iterations):
self.iterations = iterations
... | bsd-3-clause |
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